title: Hand, belt, pocket or bag: Practical activity tracking with mobile phones.
abstract: For rehabilitation and diagnoses, an understanding of patient activities and movements is important. Modern smartphones have built in accelerometers which promise to enable quantifying minute-by-minute what patients do (e.g. walk or sit). Such a capability could inform recommendations of physical activities and improve medical diagnostics. However, a major problem is that during everyday life, we carry our phone in different ways, e.g. on our belt, in our pocket, in our hand, or in a bag. The recorded accelerations are not only affected by our activities but also by the phone's location. Here we develop a method to solve this kind of problem, based on the intuition that activities change rarely, and phone locations change even less often. A hidden Markov model (HMM) tracks changes across both activities and locations, enabled by a static support vector machine (SVM) classifier that probabilistically identifies activity-location pairs. We find that this approach improves tracking accuracy on healthy subjects as compared to a static classifier alone. The obtained method can be readily applied to patient populations. Our research enables the use of phones as activity tracking devices, without the need of previous approaches to instruct subjects to always carry the phone in the same location.
authors.affiliation: Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA. Electronic address: santos@u.northwestern.edu.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Antos
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA; Center for Bionic Medicine, Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, USA.
authors.forename: Mark V
authors.initials: MV
authors.lastname: Albert
authors.affiliation: Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA; Department of Physiology, Northwestern University, Chicago, IL, USA; Department of Applied Mathematics, Northwestern University, Evanston, IL, USA.
authors.forename: Konrad P
authors.initials: KP
authors.lastname: Kording
mesh_terms: D056044:Actigraphy / Q000295:instrumentation* / Q000379:methods*; D000328:Adult; D000465:Algorithms; D040421:Cell Phone; D003020:Clothing; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D009043:Motor Activity; D010300:Parkinson Disease / Q000175:diagnosis / Q000503:physiopathology / Q000534:rehabilitation; D010865:Pilot Projects; D011187:Posture; D060388:Support Vector Machine; D016138:Walking
keywords: Accelerometer; Activity recognition; Classification; Context awareness; Parkinson's disease; Smartphone